Gaussian Process Regression-Based Material Model for Stochastic Structural Analysis
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7, Issue 3
Abstract
Data-driven material models can capture the constitutive relationship directly from the data without involving any material-dependent mathematical expressions. But most data-driven approaches, such as artificial neural networks, only estimate the deterministic relations and do not consider the material uncertainty. In this paper, the constitutive relation is taken as a stochastic function following the Gaussian process, where a probability-based nonparametric method, called Gaussian process regression (GPR), is used to capture the constitutive relation with the uncertainty being included. Both one-dimensional (1D) and two-dimensional (2D) material data are used to validate the GPR-based constitutive model (GPR model). The obtained GPR model shows higher accuracy than other data-driven approaches, particularly when the data set size is small. When compared with the assumed true model, the GPR-based model has an average relative error of . Finally, with the help of the material uncertainty identified by the GPR-based model from the material data, a data-driven stochastic structural analysis procedure is developed. The relative errors of the expected deflection and probability of failure given by the GPR model are smaller than 2% and 3%, respectively.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported in part by the Australian Research Council through the Discovery Projects Scheme (Grant No. DP190102954).
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7 • Issue 3 • September 2021
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© 2021 American Society of Civil Engineers.
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Received: May 19, 2020
Accepted: Feb 15, 2021
Published online: May 26, 2021
Published in print: Sep 1, 2021
Discussion open until: Oct 26, 2021
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